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IEEE Networking Letters 2025年第1期7卷 46-50页

作者： Sha, Zhenghao Cai, Kechao Zhang, Jinbei School of Electronics and Communication Engineering Shenzhen518107 China

Online caching at the network edge is becoming increasingly important for alleviating the transmission pressure on backbone networks. Previous studies on online caching policies mainly use the static regret as the performance metric, which relies on a fixed benchmark and lacks the capacity to ensure optimal performance in non-stationary environments. In this letter, we introduce the strongly adaptive regret into online caching and propose a Strongly Adaptive online caching scheme (SAcache). Our SAcache scheme focuses on the performance over time intervals with a length between τmin and τmax, where τmin and τmax are the lower and upper bound on how long the environment changes, respectively. SAcache consists of multiple interval caches operating in a lazy restart mode to make candidate caching decisions, and an aggregated cache that weights the these candidate decisions to determine the final caching decision. We prove that the regret upper bound is sub-linear with respect to the time interval's length τ, i.e., O(√ τ log(τmax/τmin)). Our experiment results demonstrate that SAcache achieves the highest cache hit ratio and the lowest regret compared to other caching policies in non-stationary environments. © 2019 IEEE.

关键词： Benchmark testing Upper bound Servers Adaptive systems Vectors Measurement Streaming media Research and development Redundancy Optimization

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Crowd movement monitoring in academic buildings: a reinforcement learning approach

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Multimedia Tools and Applications 2025年第10期84卷 6967-6997页

作者： Geetha, T.S. Rao, C. Subba Chellaswamy, C. Department of Electronics and Communication Engineering SA Engineering College Chennai600077 India Department of Electronics and Communication Engineering Prasad V Potluri Siddhartha Institute of Technology Vijayawada520007 India Department of Electronics and Communication Engineering SRM TRP Engineering College Tiruchirappalli621105 India

In academic institutions, the gathering of students within buildings significantly influences the ecological parameters of these spaces. Understanding and enhancing the environmental impact of academic buildings requires monitoring ecological parameters and crowd flow. The job of monitoring large-scale crowds and environmental conditions is a difficult one to do without difficulty. This study introduces reinforcement learning-based lightweight crowd flow measurement (RL-CFM) for real-time crowd flow tracking in institutional buildings. RL-CFM enables prompt responses to changes in crowd dynamics, enhancing its effectiveness during emergencies. The proposed RL-CFM periodically searches for smartphone-enabled requests, providing insights into crowd movement. Implemented and tested in an institutional building under real-world conditions, RL-CFM was installed in various locations, including the evacuation passage on the ground floor and two classrooms on the first floor. The study explores ecological parameters like temperature and CO2 concentration in the evacuation passage, considering various modes of smartphone operation that reflect people’s walking behavior. The RL-CFM’s performance is evaluated using different smartphone models with varying walking speeds, revealing a tracking accuracy of 94.32% in the Wi-Fi registered mode. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Temperature measurement

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Energy-efficient cluster-based routing in VANET assisted by hybrid jellyfish and beluga optimisation and fault tolerance

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International Journal of Vehicle Autonomous Systems 2025年第1期18卷 1-30页

作者： Mahesh, R.K. Jawaligi, Shivkumar S. Department of Electronics and Communication Engineering Faculty of Engineering and Technology Sharnbasva University Karnataka Kalaburagi India

VANET clustering solves scalability issues while fortifying the network and extending its lifespan. The following phases are included in the developed work on Energy-Efficient Cluster-based Routing (EECR) and Fuzzy-Topsis (FT) in VANET: Cluster head selection, routing and fault tolerance are the first three. Initially, the Hybrid Shuffled Shepherd Namib Beetle Optimisation Algorithm (HSS-NBO) method, which is centred on the prior work, is used to pick the cluster heads. Then, using the created Combined Jellyfish Beluga Whale Optimisation (CJ-BWO) model, routing is done as efficiently as possible while taking into account limitations such as security, trust, delay, quality of service, energy and connection quality (RSSI). Then, as a prerequisite for energy-efficient processing, the intermediate node forwarding mechanism is applied for void handling. In case of occurring faults, it is tolerated by initially detecting the faults and then proposing a recovery phase with a proxy agent Road Side Unit (RSU) deployed. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Fault tolerance

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Hybrid machine learning-based 3-dimensional UAV node localization for UAV-assisted wireless networks

Cognitive Robotics

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Cognitive Robotics 2025年 5卷 61-76页

作者： Negassa, Workeneh Geleta Gelmecha, Demissie J. Singh, Ram Sewak Rathee, Davinder Singh Electronics and Communication Engineering Adama Science and Technology University Adama Ethiopia

This paper presents a hybrid machine-learning framework for optimizing 3-Dimensional (3D) Unmanned Aerial Vehicles (UAV) node localization and resource distribution in UAV-assisted THz 6G networks to ensure efficient coverage in dynamic, high-density environments. The proposed model efficiently managed interference, adapted to UAV mobility, and ensured optimal throughput by dynamically optimizing UAV trajectories. The hybrid framework combined the strengths of Graph Neural Networks (GNN) for feature aggregation, Deep Neural Networks (DNN) for efficient resource allocation, and Double Deep Q-Networks (DDQN) for distributed decision-making. Simulation results demonstrated that the proposed model outperformed traditional machine learning models, significantly improving energy efficiency, latency, and throughput. The hybrid model achieved an optimized energy efficiency of 90 Tbps/J, reduced latency to 0.0105 ms, and delivered a network throughput of approximately 96 Tbps. The model adapts to varying link densities, maintaining stable performance even in high-density scenarios. These findings underscore the framework's potential to address key challenges in UAV-assisted 6G networks, paving the way for scalable and efficient communication in next-generation wireless systems. © 2025

关键词： Unmanned aerial vehicles (UAV)

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A robust sliding window adaptive filtering technique for phonocardiogram signal denoising

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Multimedia Tools and Applications 2025年 1-27页

作者： Shervegar, Vishwanath Madhava Department of Electronics & Communication Engineering Moodlakatte Institute of Technology Kundapura India

Phonocardiogram (PCG) signal is the digital sound recording of various heart sounds. To diagnose the different types of heart disorders, it is often necessary to analyse these PCG signals. However, PCG signal recording is challenging due to disturbing surrounding noise signals. So denoising the PCG signal is done before using PCG for advanced processing. This paper proposes a novel Sliding window Adaptive Noise Cancellers-based filter model for effectively denoising and recovering the PCG signal. This work introduces a sliding Window optimum adaptive filter structure for estimating a noise-free signal with high accuracy using Least Mean Square (LMS) algorithm. A noisy signal is processed through the Sliding Window adaptive filter stage in the proposed work. Sliding Window of fixed duration slides over the signal and in each window, the signal is filtered using the Adaptive filter. The method approximates the clean version of the PCG signal using this Sliding window adaptive filter architecture with high accuracy. The proposed robust Sliding window adaptive filter is tested against experimental PCG signals corrupted by Gaussian and pink noise with different noise levels. The experimental data are taken from the Physionet database and real world recordings. The results show that the robust Sliding Window Adaptive filter model performs remarkably well. Compared with traditional LMS filter configuration, the proposed filter structure achieves a 2–10 times reduction in MSE values. Further, there is an improved SNR by 3 times, PSNR improvement by 4%–25% comparatively. The correlation between the clean signal and its estimate is more than 0.92. Sliding Window LMS adaptive filter model offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy. In the future, such models can be tested for real-time performance for obtaining desired convergence speed and accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media,

关键词： Adaptive filters

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Transmitter diversity and OAM incorporated 40 Gbps free space optical system

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Journal of Optical communications 2025年第2期46卷 445-456页

作者： Singh, Somdeep Singh, Preeti Kaur, Pardeep University Institute of Engineering and Technology Panjab University Chandigarh India Electronics and Communication Engineering University Institute of Engineering & Technology Chandigarh India

The present research evaluates optical angular momentum's (OAM) performance in challenging atmospheric conditions and emphasizes its significance in free space optical (FSO) communication systems. It has been demonstrated that implementing the transmitter diversity (TD) technique effectively suppresses inter-channel interference, improving system performance as a whole. The best option among the studied encodings is found to be the combination of non-return-to-zero (NRZ) and carrier suppressed RZ (CSRZ), which performs better in a variety of weather scenarios and covers a wide FSO range from 72 m to 1450 m. Proposed system offered distance enhancement of 81.25 % under clear sky, 16.66 % under light rain, 10.22 % under moderate rain, 3.4 % under heavy rain, 10 % under light haze, 4 % under moderate haze, 4.44 % under heavy haze, 12.5 % under light fog, 4 % under moderate fog, 7.8 % under heavy fog, 5.8 % under light dust, 7.6 % under medium dust and 12.5 % under heavy dust as compared to existing workIn particular, during bad weather, this research offers significant insights into the design and optimisation of high-speed FSO systems. © 2024 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Fog

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Hierarchical detection and tracking for moving targets in underwater wireless sensor networks

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Digital communications and Networks 2025年第2期11卷 556-562页

作者： Yudong Li Hongcheng Zhuang Long Xu Shengquan Li Haibo Lu School of Electronics and Communication Engineering Sun Yat-sen University Peng Cheng Laboratory

It is difficult to improve both energy consumption and detection accuracy simultaneously, and even to obtain the trade-off between them, when detecting and tracking moving targets, especially for Underwater Wireless Sensor Networks(UWSNs). To this end, this paper investigates the relationship between the Degree of Target Change(DoTC) and the detection period, as well as the impact of individual nodes. A Hierarchical Detection and Tracking Approach(HDTA) is proposed. Firstly, the network detection period is determined according to DoTC, which reflects the variation of target motion. Secondly, during the network detection period, each detection node calculates its own node detection period based on the detection mutual information. Taking DoTC as pheromone, an ant colony algorithm is proposed to adaptively adjust the network detection period. The simulation results show that the proposed HDTA with the optimizations of network level and node level significantly improves the detection accuracy by 25% and the network energy consumption by 10% simultaneously, compared to the traditional adaptive period detection schemes.

关键词： Underwater wireless sensor networks The degree of target change Mutual information Pheromone Adaptive period

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Direct ink writing of nickel oxide-based thin films for room temperature gas detection

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Journal of Semiconductors 2025年第1期46卷 245-258页

作者： Neha Thakur Hari Murthy Sudha Arumugam Neethu Thomas Aarju Mathew Koshy Parasuraman Swaminathan Department of Electronics and Communication Engineering School of Engineering and TechnologyCHRIST UniversityKumbalagoduBengaluruKarnataka560074India Department of Metallurgical and Materials Engineering Indian Institute of Technology MadrasChennaiTamil Nadu600036India

The rapid industrial growth and increasing population have led to significant pollution and deterioration of the natural atmospheric *** atmospheric pollutants include NO_(2)and CO_(2).Hence,it is imperative to develop NO_(2)and CO_(2)sensors for ambient conditions,that can be used in indoor air quality monitoring,breath analysis,food spoilage detection,*** the present study,two thin film nanocomposite(nickel oxide-graphene and nickel oxide-silver nanowires)gas sensors are fabricated using direct ink *** nano-composites are investigated for their structural,optical,and electrical *** the nano-composite is deposited on the interdigitated electrode(IDE)pattern to form NO_(2)and CO_(2)*** deposited films are then exposed to NO_(2)and CO_(2)gases separately and their response and recovery times are determined using a custom-built gas sensing *** oxide-graphene provides a good response time and recovery time of 10 and 9 s,respectively for NO_(2),due to the higher electron affinity of graphene towards NO_(2).Nickel oxide-silver nanowire nano-composite is suited for CO_(2)gas because silver is an excellent electrocatalyst for CO_(2)by giving response and recovery times of 11 s *** is the first report showcasing NiO nano-composites for NO_(2)and CO_(2)sensing at room temperature.

关键词： nickel oxide graphene silver nanowires NO_(2) CO_(2) gas sensor

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Self-calibrated region-level regression for crowd counting

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Science China(Information Sciences) 2025年第4期68卷 387-388页

作者： Jiawen ZHU Wenda ZHAO You HE Huchuan LU School of Information and Communication Engineering Dalian University of Technology Department of Electronic Engineering Tsinghua University

Accurate crowd counting in natural images has become increasingly attractive owing to its numerous real-world applications, e.g., crowd analysis and video surveillance. Despite significant progress in crowd counting [1,2], challenges(such as scale variation and background clutter) *** fully utilize spatial information, existing crowd counting approaches [3, 4] mainly estimate a density map, where point annotations are smoothed via a Gaussian kernel to generate probabilities indicating the presence of a crowd.

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A novel hybrid optimization-based improved artificial intelligence methods for pancreatic disease segmentation and diagnosis

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Multimedia Tools and Applications 2025年第15期84卷 14663-14691页

作者： Yugandhar, Dasari Naidu, M.S.R. Anilkumar, B. Electronics and Communication Engineering Aditya Institute of Technology & Management Andhra Pradesh Tekkali532201 India Electronics and Communication Engineering GMR Institute of Technology Andhra Pradesh Razam532127 India

Pancreatic cancer is one of the deadliest cancers with high mortality rates as it is often diagnosed late, leading to limited treatment options. This demands an effective classification algorithm to accurately detect pancreatic cancer in its early stages. In this study, we proposed an innovative hybrid diagnostic model combining optimization and artificial intelligence algorithms using computed tomography (CT) images. The proposed work commences with the collection of CT images. Then, the images are pre-processed using the adaptive bilateral kernel filtering (ABKF) approach to enhance image quality by minimizing noise. Then, a hybrid segmentation algorithm named Lionized Red Deer Optimization-based Deconvolution Thresholding Algorithm (LRDO-DTA) was developed to segment the pancreatic disease images. Further, a ResNet-50 approach was employed for performing feature extraction, which enables the extraction of the most informative attributes from the segmented images. Finally, a golden eagle optimization-based deep belief multilayer perceptron (GEO-DBMPN) was utilized to identify pancreatic cancers. The experimental results depict that the proposed strategy achieved 97.5% Dice Similarity Coefficient (DSC), 90.5% Cancer Specificity Index (CSI), 95.19% recall, 98.12% accuracy, and obtained minimum execution time of 1.5 s. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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